Power Loss

Power loss research spans diverse applications, aiming to minimize energy waste in various systems, from electrical grids to microcontrollers and wireless energy transfer. Current efforts focus on developing accurate predictive models, employing techniques like convolutional neural networks (for material-specific loss estimation in magnetic cores) and attention-based algorithms (for predicting energy loss in IoT networks), alongside optimization algorithms like hybrid Tabu Search and Genetic Algorithms for power grid reconfiguration. These advancements are crucial for improving energy efficiency, enhancing the security of AI systems (by analyzing power consumption patterns), and enabling more reliable infrastructure management in smart grids and other critical systems.